[yorwba]

Oklab is not perceptually uniform. It's better than other color spaces with equally simple conversion functions, but in the end, it was created as a simple approximation to more complex color spaces, so compared to the best you could do, it's merely OK (hence the name).

[mattdesl]

> Oklab is not perceptually uniform

By what metric? If the target is parity with CAM16-UCS, OKLab comes closer than many color spaces also designed to be perceptually uniform.

[yorwba]

If the target is parity with CAM16-UCS, CAM16-UCS is best, tautologically. Sure, if you need a fast approximation, by all means fall back to Oklab, but that optimization isn't going to be necessary in all cases.

[mattdesl]

Obviously; but this doesn’t suggest that OKLab is not a perceptually uniform color space.

There is no “one true” UCS model - all of these are just approximations of various perception and color matching studies, and at some point CAM16-UCS will probably be made obsolete as well.

[refulgentis]

No offense, but I do find the interlocution here somewhat hard-headed.

In a sentence, color science is a science.

The words you are using have technical meanings.

When we say "Oklab isn't a perceptually accurate color system", we are not saying "it is bad" - we are saying "it is a singular matmul that is meant to imitate a perceptually accurate color system" -- and that really matters, really -- Google doesn't launch Material 3 dynamic color if we just went in on that.

The goal was singular matmul. Not perceptual accuracy.

Let me give you another tell something is really off that you'll understand intuitively.

People love quoting back the Oklab blog post, you'll also see in a sibling comment something about gradients and CAM16-UCS.

The author took two colors across the color wheel, blue and yellow, then claimed that because the CAM16-UCS gradient has gray in it, Oklab is better.

That's an absolutely insane claim.

Blue and yellow are across the color wheel from each other.

Therefore, a linear gradient between the two has to pass through the center of the color wheel.

Therefore a gradient, i.e. a lerp, will have gray in it -- if it didn't, that would be really weird and indicate some sort of fundamental issue with the color modeling.

So of course, Oklab doesn't have gray in the blue-yellow gradient, and this is written up as a good quality.

If they knew what they were talking about at the time, they wouldn't have been doing gradients in CAM16-UCS, and not done a lerp, but used the standard CSS gradient technique of "rotating" to the new point.

Because that's how you avoid gray.

Not making up a new color space, writing it up with a ton of misinfo, then leaving it up without clarification so otherwise-smart people end up completely confused for years, repeating either the blog post or "nothings perfect" ad naseum as an excuse to never engage with anything past it. They walk away with the mistaken understanding a singular matmul somehow magically blew up 50 years of color science.

I just hope this era passes within my lifetime. HSL was a tragedy. This will be worse, if it leaves the ability to do actual color science some sort of fringe slow thing in people's heads.

[mattdesl]

Yes, it's a matmul; many color models just boil down to simple math. For example, look at Li and Luo's 2024 "simple color appearance model"[1], which is very similar to OKLab (just matmul!), and created for many of the same reasons (just an approximation!). Like OKLab, it also improves upon CAM16-UCS hue linearity issues in blue. Ironically, Luo was one of the authors who proposed CAM16-UCS in 2017. And, although it certainly improves upon CAM16-UCS for many applications, I'm not yet convinced it is superior to OKLab (you can see my implementation here: [2]).

And I think you might be mis-remembering Ottosson's original blog post; he demonstrates a gradient between white and blue, not blue and yellow.

[1] https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-3-3100

[2] https://github.com/texel-org/color/blob/main/test/spaces/sim...

[refulgentis]

> Yes, it's a matmul; many color models just boil down to simple math.

All do. :)

> For example, look at Li and Luo's 2024 "simple color appearance model"[1], which is very similar to OKLab (just matmul!), and created for many of the same reasons (just an approximation!)

I don't understand what this shows me. I don't see anyone in the thread arguing there can only be one color model with one matmul. I feel self-concious I'm missing something, so I thought maybe the implication is this a real scientist working on a real space therefore our haranguing about "actual" perceptual spaces is hair-splitting, as we see a color scientist making an approximation. You also note that it is an approximation, as does the paper, so I don't think that's the case...idk :(

> Like OKLab, it also improves upon CAM16-UCS hue linearity issues in blue. Ironically, Luo was one of the authors who proposed CAM16-UCS in 2017.

What's the ironic part? (my understanding: you read this as a competition, so you find it ironic, in the colloquial sense, that the color space you perceive us advocating for, or that Oklab can replace, was created by someone who made a singular matmul type-space like Oklab in a paper?)

> I'm not yet convinced it is superior to OKLab (you can see my implementation here: [2]).

I appreciate your work and desire here and you have firey curiosity. In practice, color science uses UCS spaces to measure color difference, not render colors. (he uses CAM16-UCS and CAM16 interchangeably as well, it's confusing)

> And I think you might be mis-remembering Ottosson's original blog post; he demonstrates a gradient between white and blue, not blue and yellow.

You're right! That makes a whole lot less obvious that there's something wrong. :( Here, the sin is throwing away the whole science bit and says that's fine, look at this example.

See gradients here. https://m3.material.io/blog/science-of-color-design

Note particularly the black and white one. Gives a great sense of how much of an outlier Oklab is, and you can't fuck around with lightness that much, that's how you measure contrast.